Bibliographic record
Abstract
The best way to learn design in any field is to study examples, and some of the best examples of software design come from the tools programmers use in their own work. Software Design by Example: A Tool-Based Introduction with Python therefore builds small versions of the things programmers use in order to demystify them and give some insights into how experienced programmers think. From a file backup system and a testing framework to a regular expression matcher, a browser layout engine, and a very small compiler, we explore common design patterns, show how making code easier to test also makes it easier to reuse, and help readers understand how debuggers, profilers, package managers, and version control systems work so that they can use them more effectively. This material can be used for self-paced study, in an undergraduate course on software design, or as the core of an intensive weeklong workshop for working programmers. Each chapter has a set of exercises ranging in size and difficulty from half a dozen lines to a full day’s work. Readers should be familiar with the basics of modern Python, but the more advanced features of the language are explained and illustrated as they are introduced. All the written material in this project can be freely reused under the terms of the Creative Commons - Attribution license, while all of the software is made available under the terms of the Hippocratic License. All proceeds from sale of this book will go to support the Red Door Family Shelter in Toronto. Features: Teaches software design by showing programmers how to build the tools they use every day Each chapter includes exercises to help readers check and deepen their understanding All the example code can be downloaded, re-used, and modified under an open license
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".